{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-24T05:58:14.966880Z",
     "start_time": "2020-09-24T05:58:14.286746Z"
    }
   },
   "outputs": [],
   "source": [
    "import os, pickle, re, glob, time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from collections import Counter\n",
    "\n",
    "sns.set_style('ticks')\n",
    "pd.set_option('precision', 2)\n",
    "\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-24T06:16:03.248079Z",
     "start_time": "2020-09-24T06:16:03.245057Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi']= 300\n",
    "mpl.rc(\"savefig\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 2C (v1)\n",
    "\n",
    "- PhD_NUS\\Research\\CaDRReS2\\output\\ess_gene_pcor\n",
    "    - gdsc_drugMedianGE0_cv01_10dim_pred.csv vs gdsc_drugMedianGE0_cv01_10dim_obs.csv. For train add _train.\n",
    "    - gdsc_drugMedianGE0_cv01_wo_bp_10dim_pred.csv vs gdsc_drugMedianGE0_cv01_10dim_obs.csv. For train add _train.\n",
    "- PhD_NUS\\Research\\CaDRReS2\\data\\drug_response\\GDSC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-24T05:58:14.971400Z",
     "start_time": "2020-09-24T05:58:14.968463Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# ori_pred_fname_list = glob.glob('../../../../PhD_NUS/Research/CaDRReS2/output/ess_gene_pcor/gdsc_drugMedianGE0_cv0[1-5]_10dim_pred.csv')\n",
    "# ori_obs_fname_list = glob.glob('../../../../PhD_NUS/Research/CaDRReS2/output/ess_gene_pcor/gdsc_drugMedianGE0_cv0[1-5]_10dim_obs.csv')\n",
    "# new_pred_fname_list = glob.glob('../../../../PhD_NUS/Research/CaDRReS2/output/ess_gene_pcor/gdsc_drugMedianGE0_cv0[1-5]_wo_bp_10dim_pred.csv')\n",
    "# new_obs_fname_list = glob.glob('../../../../PhD_NUS/Research/CaDRReS2/output/ess_gene_pcor/gdsc_drugMedianGE0_cv0[1-5]_wo_bp_10dim_obs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.466894Z",
     "start_time": "2020-09-06T07:35:34.464397Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# ori_pred_df_list = []\n",
    "# ori_obs_df_list = []\n",
    "\n",
    "# new_pred_df_list = []\n",
    "# new_obs_df_list = []\n",
    "\n",
    "# for fname in sorted(ori_pred_fname_list):\n",
    "#     ori_pred_df_list.append(pd.read_csv(fname, index_col=0))\n",
    "    \n",
    "# for fname in sorted(ori_obs_fname_list):\n",
    "#     ori_obs_df_list.append(pd.read_csv(fname, index_col=0))\n",
    "    \n",
    "# for fname in sorted(new_pred_fname_list):\n",
    "#     new_pred_df_list.append(pd.read_csv(fname, index_col=0))\n",
    "    \n",
    "# for fname in sorted(new_obs_fname_list):\n",
    "#     new_obs_df_list.append(pd.read_csv(fname, index_col=0))\n",
    "    \n",
    "# drug_list = ori_pred_df_list[0].columns\n",
    "# len(drug_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.471764Z",
     "start_time": "2020-09-06T07:35:34.468800Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# def cal_scor_per_drug(obs_df_list, pred_df_list):\n",
    "\n",
    "#     k_df_list = []\n",
    "\n",
    "#     for k in range(5):\n",
    "\n",
    "#         k_obs_df = obs_df_list[k]\n",
    "#         k_pred_df = pred_df_list[k]\n",
    "#         common_samples = sorted(list(k_obs_df.index & k_pred_df.index))\n",
    "\n",
    "#         obs_mat = np.array(obs_df_list[k])\n",
    "#         pred_mat = np.array(pred_df_list[k])\n",
    "\n",
    "#         scor_list = []\n",
    "\n",
    "#         for d, _ in enumerate(drug_list):\n",
    "#             x = obs_mat[:, d]\n",
    "#             y = pred_mat[:, d]\n",
    "\n",
    "#             sel_bool = ~np.isnan(x)\n",
    "#             x = x[sel_bool]\n",
    "#             y = y[sel_bool]\n",
    "\n",
    "#             scor, _ = stats.pearsonr(x, y)\n",
    "#             scor_list.append(scor)\n",
    "\n",
    "#         k_df = pd.DataFrame(index=range(len(drug_list)))\n",
    "#         k_df.loc[:, 'drug_id'] = drug_list\n",
    "#         k_df.loc[:, 'spearman'] = scor_list\n",
    "#         k_df.loc[:, 'fold'] = k + 1\n",
    "\n",
    "#         k_df_list.append(k_df)\n",
    "\n",
    "#     scor_list = pd.concat(k_df_list).groupby('drug_id').mean().loc[drug_list, 'spearman'].values\n",
    "    \n",
    "#     return scor_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.476358Z",
     "start_time": "2020-09-06T07:35:34.473603Z"
    }
   },
   "outputs": [],
   "source": [
    "# ori_val_scor_list = cal_scor_per_drug(ori_obs_df_list, ori_pred_df_list)\n",
    "# new_val_scor_list = cal_scor_per_drug(new_obs_df_list, new_pred_df_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.480421Z",
     "start_time": "2020-09-06T07:35:34.478388Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# sns.scatterplot(ori_val_scor_list, new_val_scor_list)\n",
    "# plt.plot((0, 0.6), (0, 0.6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.484802Z",
     "start_time": "2020-09-06T07:35:34.482400Z"
    }
   },
   "outputs": [],
   "source": [
    "# stats.ttest_ind(ori_val_scor_list, new_val_scor_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.490296Z",
     "start_time": "2020-09-06T07:35:34.488118Z"
    }
   },
   "outputs": [],
   "source": [
    "# val_result_df = pd.DataFrame(np.array([ori_val_scor_list, new_val_scor_list]).T, columns=['CaDRReS', 'CaDRReS-SC'])\n",
    "# val_result_df = val_result_df.stack().reset_index().drop('level_0', axis=1)\n",
    "\n",
    "# val_result_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.495343Z",
     "start_time": "2020-09-06T07:35:34.492908Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# sns.set(font_scale=1.25, style='white')\n",
    "# fig, ax = plt.subplots(figsize=(3.5, 5))\n",
    "\n",
    "# sns.boxplot(data=val_result_df, x='level_1', y=0, fliersize=0, color='white')\n",
    "# sns.swarmplot(data=val_result_df, x='level_1', y=0, color='black', s=3, alpha=0.75)\n",
    "# # sns.violinplot(data=val_result_df, x='level_1', y=0, scale='count', inner=\"box\")\n",
    "\n",
    "# ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n",
    "\n",
    "# plt.ylim((0, 0.8))\n",
    "# plt.xlabel('')\n",
    "# plt.ylabel('Spearman correlation\\n(predicted IC50 vs observed IC50)')\n",
    "\n",
    "# plt.tight_layout()\n",
    "# plt.savefig('../figure/Figure2C.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Tranining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.499918Z",
     "start_time": "2020-09-06T07:35:34.497410Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# ori_pred_fname_list = glob.glob('../../../../PhD_NUS/Research/CaDRReS2/output/ess_gene_pcor/gdsc_drugMedianGE0_cv0[1-5]_10dim_pred_train.csv')\n",
    "# ori_obs_fname_list = glob.glob('../../../../PhD_NUS/Research/CaDRReS2/output/ess_gene_pcor/gdsc_drugMedianGE0_cv0[1-5]_10dim_obs_train.csv')\n",
    "# new_pred_fname_list = glob.glob('../../../../PhD_NUS/Research/CaDRReS2/output/ess_gene_pcor/gdsc_drugMedianGE0_cv0[1-5]_wo_bp_10dim_pred_train.csv')\n",
    "# new_obs_fname_list = glob.glob('../../../../PhD_NUS/Research/CaDRReS2/output/ess_gene_pcor/gdsc_drugMedianGE0_cv0[1-5]_wo_bp_10dim_obs_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.505017Z",
     "start_time": "2020-09-06T07:35:34.502390Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# ori_pred_df_list = []\n",
    "# ori_obs_df_list = []\n",
    "\n",
    "# new_pred_df_list = []\n",
    "# new_obs_df_list = []\n",
    "\n",
    "# for fname in sorted(ori_pred_fname_list):\n",
    "#     ori_pred_df_list.append(pd.read_csv(fname, index_col=0))\n",
    "    \n",
    "# for fname in sorted(ori_obs_fname_list):\n",
    "#     ori_obs_df_list.append(pd.read_csv(fname, index_col=0))\n",
    "    \n",
    "# for fname in sorted(new_pred_fname_list):\n",
    "#     new_pred_df_list.append(pd.read_csv(fname, index_col=0))\n",
    "    \n",
    "# for fname in sorted(new_obs_fname_list):\n",
    "#     new_obs_df_list.append(pd.read_csv(fname, index_col=0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.509337Z",
     "start_time": "2020-09-06T07:35:34.506925Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# ori_train_scor_list = cal_scor_per_drug(ori_obs_df_list, ori_pred_df_list)\n",
    "# new_train_scor_list = cal_scor_per_drug(new_obs_df_list, new_pred_df_list)\n",
    "\n",
    "# sns.scatterplot(ori_train_scor_list, new_train_scor_list)\n",
    "# plt.plot((0.3, 0.8), (0.3, 0.8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Show overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.513576Z",
     "start_time": "2020-09-06T07:35:34.511185Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# sns.scatterplot(ori_train_scor_list, ori_val_scor_list)\n",
    "# plt.plot((0, 0.6), (0, 0.6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.518636Z",
     "start_time": "2020-09-06T07:35:34.515380Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# sns.scatterplot(new_train_scor_list, new_val_scor_list)\n",
    "# plt.plot((0, 0.6), (0, 0.6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 2C (v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.665762Z",
     "start_time": "2020-09-06T07:35:34.520387Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "226"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model_spec_name_list = ['cadrres', 'cadrres-wo-sample-bias', 'cadrres-wo-sample-bias-weight']\n",
    "model_spec_name_list = ['cadrres', 'cadrres-wo-sample-bias', 'cadrres-wo-sample-bias-weight_HNSC']\n",
    "\n",
    "# score_df = pd.read_excel('../result/cv_pred/cv_score_summary.xlsx')\n",
    "score_df = pd.read_excel('../result/cv_pred_226drugs/cv_score_summary.xlsx')\n",
    "score_df = score_df[score_df['model'].isin(model_spec_name_list)]\n",
    "\n",
    "len(set(score_df['drug_id']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.671983Z",
     "start_time": "2020-09-06T07:35:34.667630Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'cadrres': 226,\n",
       "         'cadrres-wo-sample-bias': 226,\n",
       "         'cadrres-wo-sample-bias-weight_HNSC': 226})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(score_df['model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.691811Z",
     "start_time": "2020-09-06T07:35:34.674103Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drug_id</th>\n",
       "      <th>drug_name</th>\n",
       "      <th>model</th>\n",
       "      <th>precent_sensitive</th>\n",
       "      <th>log2_max_conc</th>\n",
       "      <th>log2_median_ic50</th>\n",
       "      <th>spearman</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>F1_weighted</th>\n",
       "      <th>F1_resistant</th>\n",
       "      <th>F1_sensitive</th>\n",
       "      <th>precision1_resistant</th>\n",
       "      <th>precision_sensitive</th>\n",
       "      <th>MAE_sensitive</th>\n",
       "      <th>MAE_capped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Erlotinib</td>\n",
       "      <td>cadrres</td>\n",
       "      <td>4.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.06</td>\n",
       "      <td>3.94</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Erlotinib</td>\n",
       "      <td>cadrres-wo-sample-bias</td>\n",
       "      <td>4.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.82</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Erlotinib</td>\n",
       "      <td>cadrres-wo-sample-bias-weight_HNSC</td>\n",
       "      <td>4.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>AICA Ribonucleotide</td>\n",
       "      <td>cadrres</td>\n",
       "      <td>54.14</td>\n",
       "      <td>10.97</td>\n",
       "      <td>10.86</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>AICA Ribonucleotide</td>\n",
       "      <td>cadrres-wo-sample-bias</td>\n",
       "      <td>54.14</td>\n",
       "      <td>10.97</td>\n",
       "      <td>10.86</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   drug_id            drug_name                               model  \\\n",
       "0        1            Erlotinib                             cadrres   \n",
       "1        1            Erlotinib              cadrres-wo-sample-bias   \n",
       "2        1            Erlotinib  cadrres-wo-sample-bias-weight_HNSC   \n",
       "3     1001  AICA Ribonucleotide                             cadrres   \n",
       "4     1001  AICA Ribonucleotide              cadrres-wo-sample-bias   \n",
       "\n",
       "   precent_sensitive  log2_max_conc  log2_median_ic50  spearman  accuracy  \\\n",
       "0               4.50           1.00              6.25      0.29      0.79   \n",
       "1               4.50           1.00              6.25      0.24      0.93   \n",
       "2               4.50           1.00              6.25      0.08      0.96   \n",
       "3              54.14          10.97             10.86      0.35      0.61   \n",
       "4              54.14          10.97             10.86      0.36      0.62   \n",
       "\n",
       "   F1_weighted  F1_resistant  F1_sensitive  precision1_resistant  \\\n",
       "0         0.84          0.88          0.10                  0.96   \n",
       "1         0.92          0.96          0.00                  0.95   \n",
       "2         0.93          0.98          0.00                  0.96   \n",
       "3         0.58          0.43          0.70                  0.63   \n",
       "4         0.62          0.57          0.66                  0.60   \n",
       "\n",
       "   precision_sensitive  MAE_sensitive  MAE_capped  \n",
       "0                 0.06           3.94        0.29  \n",
       "1                 0.00           4.82        0.13  \n",
       "2                 0.00           3.19        0.09  \n",
       "3                 0.60           1.68        1.34  \n",
       "4                 0.64           0.98        0.58  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.841144Z",
     "start_time": "2020-09-06T07:35:34.693565Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([50., 18.,  9., 13., 12., 13.,  9.,  7., 11.,  6.,  8.,  6.,  7.,\n",
       "         9., 11.,  9.,  8.,  8.,  6.,  6.]),\n",
       " array([ 0.  ,  4.95,  9.91, 14.86, 19.82, 24.77, 29.73, 34.68, 39.63,\n",
       "        44.59, 49.54, 54.5 , 59.45, 64.41, 69.36, 74.32, 79.27, 84.22,\n",
       "        89.18, 94.13, 99.09]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(score_df[score_df['model']==model_spec_name_list[-1]]['precent_sensitive'], bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.845349Z",
     "start_time": "2020-09-06T07:35:34.842953Z"
    }
   },
   "outputs": [],
   "source": [
    "# sns.regplot(data=score_df[score_df['model']=='cadrres-wo-sample-bias-weight'], x='precent_sensitive', y='precision_sensitive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.849734Z",
     "start_time": "2020-09-06T07:35:34.847128Z"
    }
   },
   "outputs": [],
   "source": [
    "# sns.regplot(data=score_df[score_df['model']=='cadrres'], x='precent_sensitive', y='precision_sensitive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:34.854764Z",
     "start_time": "2020-09-06T07:35:34.851697Z"
    }
   },
   "outputs": [],
   "source": [
    "label_dict = {}\n",
    "label_dict['accuracy'] = 'Accuracy'\n",
    "label_dict['MAE_sensitive'] = 'MAE (sensitive)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:35.170918Z",
     "start_time": "2020-09-06T07:35:34.856704Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_model = 'cadrres'\n",
    "y_model = 'cadrres-wo-sample-bias-weight_HNSC'\n",
    "# y_model = 'cadrres-wo-sample-bias'\n",
    "\n",
    "sns.set(font_scale=1.25, style='ticks')\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8,4))\n",
    "\n",
    "for ax, s_name in zip(axes.flatten(), ['accuracy', 'MAE_sensitive']):\n",
    "    x = score_df[score_df['model'] == x_model][s_name].values\n",
    "    y = score_df[score_df['model'] == y_model][s_name].values\n",
    "    \n",
    "    sel = [~np.isnan(x) & ~np.isnan(y)]\n",
    "    x = x[sel]\n",
    "    y = y[sel]\n",
    "    \n",
    "    min_val = np.nanmin([np.nanmin(x), np.nanmin(y)])\n",
    "    max_val = np.nanmax([np.nanmax(x), np.nanmax(y)])\n",
    "    margin = 0.05 * max_val\n",
    "    min_val = min_val-margin\n",
    "    max_val = max_val+margin\n",
    "\n",
    "    ax.scatter(x, y, s=20, color='black', linewidth=0.5, alpha=0.5)\n",
    "    ax.plot((min_val, max_val), (min_val, max_val), color='black', linestyle=':')\n",
    "    ax.set_xlim((min_val, max_val))\n",
    "    ax.set_ylim((min_val, max_val))\n",
    "    \n",
    "    if s_name in ['MAE_sensitive']:\n",
    "        polygon = patches.Polygon(np.array([(min_val, min_val), (max_val, min_val), (max_val, max_val)]),linewidth=0,edgecolor='green',facecolor='lightblue',alpha=0.5, zorder=0)\n",
    "    else:\n",
    "        polygon = patches.Polygon(np.array([(min_val, min_val), (min_val, max_val), (max_val, max_val)]),linewidth=0,edgecolor='green',facecolor='lightblue',alpha=0.5, zorder=0)\n",
    "    ax.add_patch(polygon)\n",
    "    \n",
    "    ax.set_xlabel('CaDRReS')\n",
    "    ax.set_ylabel('CaDRReS-SC')\n",
    "    \n",
    "    \n",
    "axes.flatten()[0].set_title('Accuracy')\n",
    "axes.flatten()[1].set_title('Mean Absolute Error')\n",
    "    \n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "# plt.savefig('../figure/Figure3_cadrres_vs_cadrres-sc.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:35.178372Z",
     "start_time": "2020-09-06T07:35:35.174544Z"
    }
   },
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/36874697/how-to-edit-properties-of-whiskers-fliers-caps-etc-in-seaborn-boxplot\n",
    "\n",
    "def change_boxplot_edge_color(ax, col):\n",
    "    for i, artist in enumerate(ax.artists):\n",
    "        # Set the linecolor on the artist to the facecolor, and set the facecolor to None\n",
    "        artist.set_edgecolor(col)\n",
    "        # artist.set_facecolor('None')\n",
    "\n",
    "        # Each box has 6 associated Line2D objects (to make the whiskers, fliers, etc.)\n",
    "        # Loop over them here, and use the same colour as above\n",
    "        for j in range(i*6,i*6+6):\n",
    "            line = ax.lines[j]\n",
    "            line.set_color(col)\n",
    "            line.set_mfc(col)\n",
    "            line.set_mec(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:35.544506Z",
     "start_time": "2020-09-06T07:35:35.180565Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy RanksumsResult(statistic=-3.611996473421903, pvalue=0.00030384869935654236)\n",
      "accuracy Ttest_relResult(statistic=-8.349338599928274, pvalue=7.011487666632825e-15)\n",
      "MAE_sensitive RanksumsResult(statistic=4.180568144395204, pvalue=2.9078165155403485e-05)\n",
      "MAE_sensitive Ttest_relResult(statistic=nan, pvalue=nan)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.25, style='ticks')\n",
    "# sns.set_style(\"darkgrid\", {\"axes.facecolor\": \".9\"})\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8,4))\n",
    "\n",
    "for ax, s_name in zip(axes.flatten(), ['accuracy', 'MAE_sensitive']):\n",
    "    \n",
    "    temp_df = score_df[score_df['model'].isin([x_model, y_model])]\n",
    "    sns.boxplot(data=temp_df, x='model', y=s_name, orient='v', fliersize=5, ax=ax, color='lightblue', linewidth=1.25)\n",
    "    print (s_name, stats.ranksums(temp_df[temp_df['model']==x_model][s_name], temp_df[temp_df['model']==y_model][s_name]))\n",
    "    print (s_name, stats.ttest_rel(temp_df[temp_df['model']==x_model][s_name], temp_df[temp_df['model']==y_model][s_name]))\n",
    "#     sns.swarmplot(data=temp_df, x='model', y=s_name, orient='v', s=3, linewidth=0, alpha=0.7, ax=ax, color='black')\n",
    "#     sns.violinplot(data=temp_df, y='model', x=s_name, orient='h', ax=ax, color='grey', inner='stick')\n",
    "    \n",
    "    ax.set_ylabel(label_dict[s_name])\n",
    "    change_boxplot_edge_color(ax, 'black')\n",
    "    \n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "# plt.savefig('../figure/Figure3_cadrres_vs_cadrres-sc_boxplot.svg')\n",
    "plt.savefig('../figure/supplementary_cadrres_vs_cadrres-wo-sample-bias_boxplot.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:35.566191Z",
     "start_time": "2020-09-06T07:35:35.546128Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy\n",
      "0.7300884955752213 0.26991150442477874\n",
      "0.0648460445436997\n",
      "MAE_sensitive\n",
      "0.2610619469026549 0.7300884955752213\n",
      "-0.11755733250085663\n"
     ]
    }
   ],
   "source": [
    "m1 = x_model\n",
    "m2 = y_model\n",
    "\n",
    "for s_name in ['accuracy', 'MAE_sensitive']:\n",
    "    s_df = score_df[['drug_id', 'model', s_name]].pivot(index='drug_id', columns='model', values=s_name)\n",
    "    print (s_name)\n",
    "    \n",
    "    print (np.sum(s_df[m1] <= s_df[m2]) / s_df[m1].shape[0], np.sum(s_df[m1] > s_df[m2]) / s_df[m1].shape[0])\n",
    "    print (np.mean((s_df[m2] - s_df[m1]) / s_df[m1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:35.573949Z",
     "start_time": "2020-09-06T07:35:35.567906Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(s_df[m2].isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate improvement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:35.591826Z",
     "start_time": "2020-09-06T07:35:35.575881Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>model</th>\n",
       "      <th>cadrres</th>\n",
       "      <th>cadrres-wo-sample-bias</th>\n",
       "      <th>cadrres-wo-sample-bias-weight_HNSC</th>\n",
       "      <th>increase</th>\n",
       "      <th>acc_increase_rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drug_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>0.48</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.32</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.27</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>0.71</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.24</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.22</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "model    cadrres  cadrres-wo-sample-bias  cadrres-wo-sample-bias-weight_HNSC  \\\n",
       "drug_id                                                                        \n",
       "110         0.48                    0.69                                0.81   \n",
       "17          0.60                    0.86                                0.92   \n",
       "59          0.64                    0.87                                0.91   \n",
       "94          0.71                    0.89                                0.95   \n",
       "111         0.51                    0.60                                0.73   \n",
       "\n",
       "model    increase  acc_increase_rank  \n",
       "drug_id                               \n",
       "110          0.34                  1  \n",
       "17           0.32                  2  \n",
       "59           0.27                  3  \n",
       "94           0.24                  4  \n",
       "111          0.22                  5  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_df = score_df.pivot(index='drug_id', columns='model', values='accuracy')\n",
    "accuracy_df.loc[:, 'increase'] = accuracy_df['cadrres-wo-sample-bias-weight_HNSC'] - accuracy_df['cadrres']\n",
    "accuracy_df = accuracy_df.sort_values('increase', ascending=False)\n",
    "accuracy_df.loc[:, 'acc_increase_rank'] = [i+1 for i in range(accuracy_df.shape[0])]\n",
    "accuracy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:35.609114Z",
     "start_time": "2020-09-06T07:35:35.593480Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>model</th>\n",
       "      <th>cadrres</th>\n",
       "      <th>cadrres-wo-sample-bias</th>\n",
       "      <th>cadrres-wo-sample-bias-weight_HNSC</th>\n",
       "      <th>increase</th>\n",
       "      <th>mae_increase_rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drug_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>7.72</td>\n",
       "      <td>5.99</td>\n",
       "      <td>1.71</td>\n",
       "      <td>6.01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>9.57</td>\n",
       "      <td>10.85</td>\n",
       "      <td>5.54</td>\n",
       "      <td>4.02</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>6.75</td>\n",
       "      <td>7.37</td>\n",
       "      <td>3.71</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>5.86</td>\n",
       "      <td>7.03</td>\n",
       "      <td>2.97</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>4.96</td>\n",
       "      <td>6.15</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.84</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "model    cadrres  cadrres-wo-sample-bias  cadrres-wo-sample-bias-weight_HNSC  \\\n",
       "drug_id                                                                        \n",
       "1018        7.72                    5.99                                1.71   \n",
       "197         9.57                   10.85                                5.54   \n",
       "1072        6.75                    7.37                                3.71   \n",
       "1029        5.86                    7.03                                2.97   \n",
       "166         4.96                    6.15                                2.12   \n",
       "\n",
       "model    increase  mae_increase_rank  \n",
       "drug_id                               \n",
       "1018         6.01                  1  \n",
       "197          4.02                  2  \n",
       "1072         3.05                  3  \n",
       "1029         2.89                  4  \n",
       "166          2.84                  5  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae_df = score_df.pivot(index='drug_id', columns='model', values='MAE_sensitive')\n",
    "mae_df.loc[:, 'increase'] = mae_df['cadrres'] - mae_df['cadrres-wo-sample-bias-weight_HNSC']\n",
    "mae_df = mae_df.sort_values('increase', ascending=False)\n",
    "mae_df.loc[:, 'mae_increase_rank'] = [i+1 for i in range(mae_df.shape[0])]\n",
    "mae_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:35.622491Z",
     "start_time": "2020-09-06T07:35:35.610795Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "drug_id\n",
       "1        27.0\n",
       "255      27.0\n",
       "1129     27.0\n",
       "279      33.0\n",
       "171      37.5\n",
       "        ...  \n",
       "41      211.0\n",
       "87      214.5\n",
       "60      216.5\n",
       "11      216.5\n",
       "9       219.5\n",
       "Length: 226, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "improve_rank_df = pd.merge(accuracy_df[['acc_increase_rank']], mae_df[['mae_increase_rank']], left_index=True, right_index=True)\n",
    "\n",
    "improve_rank_df.mean(axis=1).sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 2C (v2 HNSC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:35.626387Z",
     "start_time": "2020-09-06T07:35:35.624077Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# # model_spec_name_list = ['cadrres', 'cadrres-wo-sample-bias', 'cadrres-wo-sample-bias-weight']\n",
    "# model_spec_name_list = ['cadrres', 'cadrres-wo-sample-bias-weight_HNSC']\n",
    "\n",
    "# score_df = pd.read_excel('../result/cv_pred/cv_score_summary_HNSC.xlsx')\n",
    "# score_df = score_df[score_df['precent_sensitive'] >= 50]\n",
    "# score_df = score_df[score_df['model'].isin(model_spec_name_list)]\n",
    "\n",
    "# len(set(score_df['drug_id']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:35.631044Z",
     "start_time": "2020-09-06T07:35:35.628224Z"
    }
   },
   "outputs": [],
   "source": [
    "# plt.hist(score_df[score_df['model']=='cadrres']['precent_sensitive'], bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:35.636435Z",
     "start_time": "2020-09-06T07:35:35.632892Z"
    },
    "code_folding": [
     0,
     6
    ]
   },
   "outputs": [],
   "source": [
    "# x_model = 'cadrres'\n",
    "# y_model = 'cadrres-wo-sample-bias-weight_HNSC'\n",
    "# # y_model = 'cadrres-wo-sample-bias'\n",
    "\n",
    "# fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8,4))\n",
    "\n",
    "# for ax, s_name in zip(axes.flatten(), ['accurary', 'MAE']):\n",
    "#     x = score_df[score_df['model'] == x_model][s_name].values\n",
    "#     y = score_df[score_df['model'] == y_model][s_name].values\n",
    "    \n",
    "#     sel = [~np.isnan(x) & ~np.isnan(y)]\n",
    "#     x = x[sel]\n",
    "#     y = y[sel]\n",
    "    \n",
    "#     min_val = np.nanmin([np.nanmin(x), np.nanmin(y)])\n",
    "#     max_val = np.nanmax([np.nanmax(x), np.nanmax(y)])\n",
    "#     margin = 0.05 * max_val\n",
    "#     min_val = min_val-margin\n",
    "#     max_val = max_val+margin\n",
    "\n",
    "#     ax.scatter(x, y, s=20, color='black', linewidth=0.5, alpha=0.5)\n",
    "#     ax.plot((min_val, max_val), (min_val, max_val), color='black', linestyle=':')\n",
    "#     ax.set_xlim((min_val, max_val))\n",
    "#     ax.set_ylim((min_val, max_val))\n",
    "    \n",
    "#     if s_name in ['MAE']:\n",
    "#         polygon = patches.Polygon(np.array([(min_val, min_val), (max_val, min_val), (max_val, max_val)]),linewidth=0,edgecolor='green',facecolor='lightblue',alpha=0.2)\n",
    "#     else:\n",
    "#         polygon = patches.Polygon(np.array([(min_val, min_val), (min_val, max_val), (max_val, max_val)]),linewidth=0,edgecolor='green',facecolor='lightblue',alpha=0.2)\n",
    "#     ax.add_patch(polygon)\n",
    "    \n",
    "#     ax.set_xlabel('CaDRReS')\n",
    "#     ax.set_ylabel('CaDRReS-SC')\n",
    "\n",
    "# axes.flatten()[0].set_title('Accurary')\n",
    "# axes.flatten()[1].set_title('Mean Absolute Error')\n",
    "    \n",
    "# plt.tight_layout()\n",
    "# plt.savefig('../figure/Figure2C_scatter_HNSC.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:35.641083Z",
     "start_time": "2020-09-06T07:35:35.638397Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# stacked_score_df = score_df.set_index(['drug_id', 'model']).stack().reset_index()\n",
    "# stacked_score_df.columns = ['drug_id', 'model', 'score_name', 'score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:35.645895Z",
     "start_time": "2020-09-06T07:35:35.642862Z"
    },
    "code_folding": [
     0
    ],
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# sns.set(font_scale=1.25, style='ticks')\n",
    "\n",
    "# fig, ax = plt.subplots(figsize=(3.5, 5))\n",
    "\n",
    "# s_name = 'MAE' # accurary, spearman | MAE | f1 | f1_sensitive | precision_sensitive\n",
    "\n",
    "# df = stacked_score_df[stacked_score_df['score_name']==s_name]\n",
    "\n",
    "# pivot_df = df.pivot(index='drug_id', columns='model', values='score')\n",
    "# x = pivot_df[pivot_df.columns[0]].values\n",
    "# y = pivot_df[pivot_df.columns[1]].values\n",
    "# print (stats.ttest_rel(x, y))\n",
    "\n",
    "# # sns.violinplot(data=df, x='model', y='score', color='lightgrey', inner='box', ax=ax)\n",
    "# sns.swarmplot(data=df, x='model', y='score', color='black', s=3, alpha=.75, ax=ax)\n",
    "# sns.boxplot(data=df, x='model', y='score', color='white', fliersize=0, ax=ax)\n",
    "# ax.set_xticklabels(['CaDRReS', 'CaDRReS-SC'], rotation=45, ha='right') # 'CaDRReS\\nno-bias', \n",
    "\n",
    "# ax.set_ylabel(s_name[0].upper() + s_name[1:])\n",
    "# ax.set_xlabel('')\n",
    "\n",
    "# # ax.set_ylim((0.5, 4.5))\n",
    "# # ax.set_ylim((0, 0.6))\n",
    "# # ax.set_ylim((0.4, 1.09))\n",
    "    \n",
    "# plt.tight_layout()\n",
    "# plt.savefig('../figure/Figure2C_{}_HNSC.svg'.format(s_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supplementary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-24T05:58:25.665478Z",
     "start_time": "2020-09-24T05:58:25.663040Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.patches as patches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-24T05:58:25.701090Z",
     "start_time": "2020-09-24T05:58:25.698528Z"
    }
   },
   "outputs": [],
   "source": [
    "def logistic(x_vals, x0, k):\n",
    "    return 100 / (1 + np.e**(-k * (x_vals - x0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-24T05:58:26.859048Z",
     "start_time": "2020-09-24T05:58:26.253717Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-2.5, -0.5, 3.5, 5.0]\n",
      "99.33071490757152\n",
      "62.245933120185455\n",
      "0.09110511944006457\n",
      "0.004539786870243442\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x324 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "size = 7.5\n",
    "\n",
    "dosages = np.arange(-size, size, 0.1)\n",
    "\n",
    "response_dict = {}\n",
    "response_dict['i'] = logistic(dosages, 5-size, 2)\n",
    "response_dict['j'] = logistic(dosages, 7-size, 1)\n",
    "response_dict['k'] = logistic(dosages, 11-size, 2)\n",
    "response_dict['l'] = logistic(dosages, 12.5-size, 2)\n",
    "\n",
    "print ([5-size, 7-size, 11-size, 12.5-size])\n",
    "print (logistic(0, 5-size, 2))\n",
    "print (logistic(0, 7-size, 1))\n",
    "print (logistic(0, 11-size, 2))\n",
    "print (logistic(0, 12.5-size, 2))\n",
    "\n",
    "color_dict = {}\n",
    "color_dict['i'] = 'seagreen'\n",
    "color_dict['j'] = 'lightseagreen'\n",
    "color_dict['k'] = 'sienna'\n",
    "color_dict['l'] = 'chocolate'\n",
    "\n",
    "sns.set(font_scale=1.5, style='white')\n",
    "fig, ax = plt.subplots(figsize=(6, 4.5))\n",
    "\n",
    "for d, resp in response_dict.items():\n",
    "    ax.plot(dosages, resp, label=d, linewidth=2.5, alpha=0.7, color=color_dict[d])\n",
    "    \n",
    "ax.axhline(y=50, linestyle='--', color='grey')\n",
    "    \n",
    "rect = patches.Rectangle((-size,-5),size,110,linewidth=0,edgecolor='green',facecolor='lightblue', hatch='///', alpha=0.2)\n",
    "ax.add_patch(rect)\n",
    "    \n",
    "ax.set_xlim((-size, size))\n",
    "ax.set_ylim((-5, 105))\n",
    "ax.set_xlabel('Drug dosage ' + r'$log_2(\\mu M)$')\n",
    "ax.set_ylabel('% cell death')\n",
    "plt.legend(fontsize=15)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('../figure/supplementary_dose_response_curve.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-24T12:57:18.492260Z",
     "start_time": "2020-09-24T12:57:17.719775Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-4, -2, 1]\n",
      "99.96646498695337\n",
      "88.07970779778823\n",
      "11.920292202211758\n"
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      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "size = 9\n",
    "\n",
    "dosages = np.arange(-size, size, 0.1)\n",
    "exp_dosages = np.arange(-8, 1, 1)\n",
    "\n",
    "response_dict = {}\n",
    "response_dict['i'] = logistic(dosages, 5-size, 2)\n",
    "response_dict['j'] = logistic(dosages, 8-size, 1)\n",
    "response_dict['k'] = logistic(dosages, 10-size, 2)\n",
    "\n",
    "exp_dict = {}\n",
    "np.random.seed(1)\n",
    "noise_level = 10\n",
    "exp_dict['i'] = logistic(exp_dosages, 5-size, 2) + ((np.random.random(exp_dosages.shape[0]) - 0.5) * noise_level)\n",
    "exp_dict['j'] = logistic(exp_dosages, 8-size, 1) + ((np.random.random(exp_dosages.shape[0]) - 0.5) * noise_level)\n",
    "exp_dict['k'] = logistic(exp_dosages, 10-size, 2) + ((np.random.random(exp_dosages.shape[0]) - 0.5) * noise_level)\n",
    "\n",
    "print ([5-size, 7-size, 10-size])\n",
    "print (logistic(0, 5-size, 2))\n",
    "print (logistic(0, 7-size, 1))\n",
    "print (logistic(0, 10-size, 2))\n",
    "\n",
    "color_dict = {}\n",
    "color_dict['i'] = 'seagreen'\n",
    "color_dict['j'] = 'lightseagreen'\n",
    "color_dict['k'] = 'sienna'\n",
    "color_dict['l'] = 'chocolate'\n",
    "\n",
    "sns.set(font_scale=1.2, style='white')\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "\n",
    "for d, resp in response_dict.items():\n",
    "    ax.plot(dosages[dosages <= 0.0], resp[dosages <= 0.0], linewidth=2.5, alpha=0.5, color=color_dict[d], linestyle='-')\n",
    "    ax.plot(dosages[dosages > 0.0], resp[dosages > 0.0], linewidth=2.5, alpha=0.5, color=color_dict[d], linestyle=':')\n",
    "    ax.scatter(exp_dosages, exp_dict[d], marker='X', color=color_dict[d], label=d)\n",
    "    \n",
    "ax.axhline(y=50, linestyle='--', color='grey', zorder=0)\n",
    "    \n",
    "# rect = patches.Rectangle((-size,-5),size,110,linewidth=0,edgecolor='green',facecolor='lightblue', hatch='///', alpha=0.2)\n",
    "# ax.add_patch(rect)\n",
    "    \n",
    "ax.set_xlim((-size, 4))\n",
    "ax.set_ylim((-5, 105))\n",
    "ax.set_xlabel('Drug dosage ' + r'$log_2(\\mu M)$')\n",
    "ax.set_ylabel('% cell death')\n",
    "# plt.legend(fontsize=15)\n",
    "\n",
    "plt.tight_layout()\n",
    "sns.despine()\n",
    "\n",
    "plt.savefig('../figure/supplementary_dose_response_curve_annotated.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-06T07:35:36.817995Z",
     "start_time": "2020-09-06T07:35:36.388633Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x324 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5, style='white')\n",
    "fig, ax = plt.subplots(figsize=(6, 4.5))\n",
    "\n",
    "y = 1 - (logistic(dosages, 0.5, 10) / 100)\n",
    "ax.plot(dosages, y, linewidth=2.5, alpha=0.7, color='black')\n",
    "    \n",
    "# ax.axhline(y=50, linestyle='--', color='grey')\n",
    "    \n",
    "rect = patches.Rectangle((-size,-5),size,110,linewidth=0,edgecolor='green',facecolor='lightblue', hatch='///', alpha=0.2)\n",
    "ax.add_patch(rect)\n",
    "    \n",
    "ax.set_xlim((-size, size))\n",
    "ax.set_ylim((-0.05, 1.05))\n",
    "ax.set_xlabel(r'$d_i$' + ' dosage ' + r'$log_2(\\mu M)$')\n",
    "ax.set_ylabel(r'$c_{iu}$')\n",
    "# plt.legend(fontsize=15)\n",
    "\n",
    "sns.despine()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../figure/supplementary_dose_weight.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 2D\n",
    "\n",
    "- CaDRReS_package/notebook/cadrres_train_models_sample_weight.ipynb\n",
    "- CaDRReS_package/notebook/todo_cadrres_predict_with_gdsc_model.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cadrres_model_dict = pickle.load(open('../../CaDRReS_package/output_weighted/hn_84_drug_cw_dw10_100000_param_dict.pickle', 'rb'))\n",
    "# cadrres_output_dict = pickle.load(open('../../CaDRReS_package/output_weighted/hn_84_drug_cw_dw10_100000_output_dict.pickle', 'rb'))\n",
    "\n",
    "cadrres_model_dict = pickle.load(open('../result/HN_model/hn_drug_cw_dw10_100000_param_dict.pickle', 'rb'))\n",
    "cadrres_output_dict = pickle.load(open('../result/HN_model/hn_drug_cw_dw10_100000_output_dict.pickle', 'rb'))\n",
    "\n",
    "# cadrres_model_dict = pickle.load(open('../result/HN_model/hn_drug_cw_dw1_100000_param_dict.pickle', 'rb'))\n",
    "# cadrres_output_dict = pickle.load(open('../result/HN_model/hn_drug_cw_dw1_100000_output_dict.pickle', 'rb'))\n",
    "\n",
    "# cadrres_model_dict = pickle.load(open('../result/HN_model/hn_drug_cw_dwsim10_100000_param_dict.pickle', 'rb'))\n",
    "# cadrres_output_dict = pickle.load(open('../result/HN_model/hn_drug_cw_dwsim10_100000_output_dict.pickle', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdsc_sample_df = pd.read_csv('../data/GDSC/GDSC_tissue_info.csv', index_col=0)\n",
    "gdsc_sample_df.index = gdsc_sample_df.index.astype(str)\n",
    "\n",
    "gdsc_sample_list = pd.read_csv('../data/GDSC/gdsc_all_abs_ic50_bayesian_sigmoid_only9dosages.csv', index_col=0).index.astype(str)\n",
    "\n",
    "gdsc_hn_sample_list = gdsc_sample_df[gdsc_sample_df['TCGA_CLASS']=='HNSC'].index\n",
    "\n",
    "gdsc_drug_df = pd.read_csv('../preprocessed_data/GDSC/drug_stat.csv', index_col=0)\n",
    "gdsc_drug_df.index = gdsc_drug_df.index.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>model</th>\n",
       "      <th>acc_increase_rank</th>\n",
       "      <th>mae_increase_rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>138</td>\n",
       "      <td>21</td>\n",
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       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>118</td>\n",
       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>140</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>141</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149</th>\n",
       "      <td>158</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1060</th>\n",
       "      <td>119</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>56</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>199</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>101</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>117</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "model    acc_increase_rank  mae_increase_rank\n",
       "Drug ID                                      \n",
       "1010                   138                 21\n",
       "1032                   118                 27\n",
       "1012                   140                 33\n",
       "157                    141                 39\n",
       "1149                   158                 46\n",
       "...                    ...                ...\n",
       "1060                   119                202\n",
       "177                     56                203\n",
       "1031                   199                204\n",
       "1058                   101                212\n",
       "1016                   117                218\n",
       "\n",
       "[81 rows x 2 columns]"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "improve_rank_df.loc[cadrres_output_dict['obs_train_df'].columns.astype(int)].sort_values('mae_increase_rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [],
   "source": [
    "drug_id = '201'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drug Name                                         Epothilone B\n",
       "Synonyms                  Patupilone, EpoB, EPO906, GNF-PF-193\n",
       "Target                                  Microtubule stabiliser\n",
       "Target Pathway                                         Mitosis\n",
       "Selleckchem Cat#                                         S1364\n",
       "CAS number                                         152044-54-7\n",
       "PubCHEM                                                 448013\n",
       "Others                                                     NaN\n",
       "entropy                                                    4.1\n",
       "max_conc                                                 0.032\n",
       "log2_max_conc                                               -5\n",
       "num_cl                                                     931\n",
       "num_cl_hn                                                   42\n",
       "log2_median_ic50                                          -7.3\n",
       "median_ic50                                             0.0065\n",
       "median_ic50_3f                                          0.0022\n",
       "log2_median_ic50_3f                                       -8.9\n",
       "median_ic50_9f                                         0.00072\n",
       "log2_median_ic50_9f                                        -10\n",
       "log2_median_ic50_hn                                       -8.5\n",
       "median_ic50_hn                                          0.0027\n",
       "median_ic50_3f_hn                                      0.00091\n",
       "log2_median_ic50_3f_hn                                     -10\n",
       "median_ic50_9f_hn                                       0.0003\n",
       "log2_median_ic50_9f_hn                                     -12\n",
       "num_sensitive                                              699\n",
       "num_sensitive_hn                                            31\n",
       "Name: 201, dtype: object"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdsc_drug_df.loc[drug_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.640559199361471 6.985058847117078e-102\n"
     ]
    }
   ],
   "source": [
    "x_all = np.array(cadrres_output_dict['obs_train_df'][[drug_id]]).flatten()\n",
    "y_all = np.array(cadrres_output_dict['pred_train_df'][[drug_id]]).flatten()\n",
    "scor_all, pval_all = stats.spearmanr(x_all[~np.isnan(x_all)], y_all[~np.isnan(x_all)])\n",
    "print (scor_all, pval_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(cadrres_output_dict['obs_train_df'].loc[gdsc_hn_sample_list, drug_id]).flatten()\n",
    "y = np.array(cadrres_output_dict['pred_train_df'].loc[gdsc_hn_sample_list, drug_id]).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.25, style='white')\n",
    "fig, ax = plt.subplots(figsize=(5, 4))\n",
    "\n",
    "\n",
    "ax.scatter(x_all[~np.isnan(x_all)], y_all[~np.isnan(x_all)], s=20, color='black', linewidth=0, alpha=0.5, label='All')\n",
    "# ax.scatter(x[~np.isnan(x)], y[~np.isnan(x)], color='orange', linewidth=0, s=25, alpha=0.8, label='Head and neck')\n",
    "# ax.axvline(x=gdsc_drug_df.loc[drug_id, 'log2_max_conc'])\n",
    "\n",
    "ax.set_xlabel(r'Observed $log_2(IC_{50})$')\n",
    "ax.set_ylabel(r'Predicted $log_2(IC_{50})$')\n",
    "\n",
    "margin = 1\n",
    "min_val = np.nanmin(np.concatenate([x_all, y_all]))-margin\n",
    "max_val = np.nanmax(np.concatenate([x_all, y_all]))+margin\n",
    "\n",
    "ax.plot([min_val-margin, max_val+margin], [min_val-margin, max_val+margin], ls=\"--\", c=\".3\")\n",
    "\n",
    "max_conc = gdsc_drug_df.loc[drug_id, 'log2_max_conc']\n",
    "# rect = patches.Rectangle((min_val, min_val), np.abs(min_val-max_conc) ,np.abs(min_val-max_conc),linewidth=0,edgecolor='green',facecolor='lightblue', hatch='///', alpha=0.2)\n",
    "rect = patches.Rectangle((min_val, min_val), np.abs(min_val-max_conc) ,np.abs(min_val-max_conc),linewidth=0,edgecolor='lightblue',facecolor='lightblue', alpha=0.5, zorder=0)\n",
    "ax.add_patch(rect)\n",
    "\n",
    "plt.xlim((min_val, max_val))\n",
    "plt.ylim((min_val, max_val-12))\n",
    "# plt.ylim((-y_size, y_size))\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.rcParams['legend.title_fontsize'] = 14\n",
    "# plt.legend(fontsize=14, handletextpad=0.03, title=\"Afatinib (ERBB2)\")\n",
    "\n",
    "sns.despine()\n",
    "plt.savefig('../figure/Figure3C.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
